IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/54785.html
   My bibliography  Save this paper

An Odd Couple: Monotone Instrumental Variables and Binary Treatments

Author

Listed:
  • Richey, Jeremiah

Abstract

This paper investigates Monotone Instrumental Variables (MIV) and their ability to aid in identifying treatment effects when the treatment is binary in a nonparametric bounding framework. I show that an MIV can only aid in identification beyond that of a Monotone Treatment Selection assumption if for some region of the instrument the observed conditional-on-received-treatment outcomes exhibit monotonicity in the instrument in the opposite direction as that assumed by the MIV in a Simpson's Paradox-like fashion. Furthermore, an MIV can only aid in identification beyond that of a Monotone Treatment Response assumption if for some region of the instrument either the above Simpson's Paradox-like relationship exists or the instrument's indirect effect on the outcome (as through its influence on treatment selection) is the opposite of its direct effect as assumed by the MIV. The implications of the main findings for empirical work are discussed and the results are highlighted with an application investigating the effect of criminal convictions on job match quality using data from the 1997 National Longitudinal Survey of the Youth. Though the main results are shown to hold only for the binary treatment case in general, they are shown to have important implications for the multi-valued treatment case as well.

Suggested Citation

  • Richey, Jeremiah, 2013. "An Odd Couple: Monotone Instrumental Variables and Binary Treatments," MPRA Paper 54785, University Library of Munich, Germany, revised 06 Nov 2013.
    • Handle: RePEc:pra:mprapa:54785
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/54785/1/MPRA_paper_54785.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/56999/1/MPRA_paper_54785.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kreider, Brent & Pepper, John V., 2007. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 432-441, June.
    2. Black, Dan A, 1995. "Discrimination in an Equilibrium Search Model," Journal of Labor Economics, University of Chicago Press, vol. 13(2), pages 309-333, April.
    3. Michael Gerfin & Martin Schellhorn, 2006. "Nonparametric bounds on the effect of deductibles in health care insurance on doctor visits – Swiss evidence," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 1011-1020, September.
    4. Changhui Kang, 2011. "Family Size and Educational Investments in Children: Evidence from Private Tutoring Expenditures in South Korea," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(1), pages 59-78, February.
    5. Holzer, Harry J., 2007. "Collateral Costs: The Effects of Incarceration on the Employment and Earnings of Young Workers," IZA Discussion Papers 3118, Institute of Labor Economics (IZA).
    6. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    7. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    8. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    9. Libertad González, 2005. "Nonparametric bounds on the returns to language skills," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 771-795.
    10. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    11. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    12. Monique de Haan, 2011. "The Effect of Parents' Schooling on Child's Schooling: A Nonparametric Bounds Analysis," Journal of Labor Economics, University of Chicago Press, vol. 29(4), pages 859-892.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Richey, Jeremiah, 2015. "Shackled labor markets: Bounding the causal effects of criminal convictions in the U.S," International Review of Law and Economics, Elsevier, vol. 41(C), pages 17-24.
    2. Siwach, Garima, 2017. "Criminal background checks and recidivism: Bounding the causal impact," International Review of Law and Economics, Elsevier, vol. 52(C), pages 74-85.
    3. Aizawa, T.;, 2019. "Reviewing the Existing Evidence of the Conditional Cash Transfer in India through the Partial Identification Approach," Health, Econometrics and Data Group (HEDG) Working Papers 19/24, HEDG, c/o Department of Economics, University of York.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsunao Okumura & Emiko Usui, 2014. "Concave‐monotone treatment response and monotone treatment selection: With an application to the returns to schooling," Quantitative Economics, Econometric Society, vol. 5, pages 175-194, March.
    2. Stefan Boes, 2009. "Bounds on Counterfactual Distributions Under Semi-Monotonicity Constraints," SOI - Working Papers 0920, Socioeconomic Institute - University of Zurich.
    3. Aizawa, T.;, 2019. "Reviewing the Existing Evidence of the Conditional Cash Transfer in India through the Partial Identification Approach," Health, Econometrics and Data Group (HEDG) Working Papers 19/24, HEDG, c/o Department of Economics, University of York.
    4. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    5. Lukáš Lafférs, 2019. "Bounding average treatment effects using linear programming," Empirical Economics, Springer, vol. 57(3), pages 727-767, September.
    6. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    7. Sianesi, Barbara, 2017. "Evidence of randomisation bias in a large-scale social experiment: The case of ERA," Journal of Econometrics, Elsevier, vol. 198(1), pages 41-64.
    8. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae Lee, 2018. "The identification power of smoothness assumptions in models with counterfactual outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 617-642, July.
    9. Lafférs, Lukáš, 2013. "A note on bounding average treatment effects," Economics Letters, Elsevier, vol. 120(3), pages 424-428.
    10. Christelis, Dimitris & Dobrescu, Loretti I., 2020. "The causal effect of social activities on cognition: Evidence from 20 European countries," Social Science & Medicine, Elsevier, vol. 247(C).
    11. Kamila Cygan‐Rehm & Daniel Kuehnle & Michael Oberfichtner, 2017. "Bounding the causal effect of unemployment on mental health: Nonparametric evidence from four countries," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1844-1861, December.
    12. Xu, Chen & Liu, Xiao, 2023. "The economic value of language in China: How important is Mandarin proficiency in the Chinese labor market? A bounding approach," Labour Economics, Elsevier, vol. 84(C).
    13. Monique De Haan & Edwin Leuven, 2020. "Head Start and the Distribution of Long-Term Education and Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 38(3), pages 727-765.
    14. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.
    15. Lukáš Lafférs, 2019. "Identification in Models with Discrete Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 657-696, February.
    16. Didier Nibbering & Matthijs Oosterveen, 2023. "Instrument-based estimation of full treatment effects with movers," Papers 2306.07018, arXiv.org.
    17. Balat, Jorge F. & Han, Sukjin, 2023. "Multiple treatments with strategic substitutes," Journal of Econometrics, Elsevier, vol. 234(2), pages 732-757.
    18. Stefan Boes, 2009. "Partial Identification of Discrete Counterfactual Distributions with Sequential Update of Information," SOI - Working Papers 0918, Socioeconomic Institute - University of Zurich.
    19. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    20. Battistin, Erich & De Nadai, Michele & Vuri, Daniela, 2017. "Counting rotten apples: Student achievement and score manipulation in Italian elementary Schools," Journal of Econometrics, Elsevier, vol. 200(2), pages 344-362.

    More about this item

    Keywords

    Instrumental variables; Nonparametric bounds; Partial identification; Criminal convictions;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:54785. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.